

import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np



class        _a		:


					'''simple docstring'''






					def     __A     (					self     ):
						torch.manual_seed(0     )
						A__				:       Tuple	  =						TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""     )

						torch.manual_seed(0     )
						A__				:       Tuple	  =						AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""     )

						torch.manual_seed(0     )
						A__				:       Tuple	  =						UNetaDConditionModel(
						    sample_size=32       ,			layers_per_block=1       ,			block_out_channels=[32, 64]       ,			down_block_types=[
						        """ResnetDownsampleBlock2D""",
						        """SimpleCrossAttnDownBlock2D""",
						    ]       ,			mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""       ,			up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]       ,			in_channels=3       ,			out_channels=6       ,			cross_attention_dim=32       ,			encoder_hid_dim=32       ,			attention_head_dim=8       ,			addition_embed_type="""text"""       ,			addition_embed_type_num_heads=2       ,			cross_attention_norm="""group_norm"""       ,			resnet_time_scale_shift="""scale_shift"""       ,			act_fn="""gelu"""       ,			)
						unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

						torch.manual_seed(0     )
						A__				:       Union[str, Any]	  =						DDPMScheduler(
						    num_train_timesteps=1000       ,			beta_schedule="""squaredcos_cap_v2"""       ,			beta_start=0.0_0_0_1       ,			beta_end=0.0_2       ,			thresholding=a_       ,			dynamic_thresholding_ratio=0.9_5       ,			sample_max_value=1.0       ,			prediction_type="""epsilon"""       ,			variance_type="""learned_range"""       ,			)

						torch.manual_seed(0     )
						A__				:       Tuple	  =						IFWatermarker()

						return {
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "unet": unet,
						    "scheduler": scheduler,
						    "watermarker": watermarker,
						    "safety_checker": None,
						    "feature_extractor": None,
						}






					def     __A     (					self     ):
						torch.manual_seed(0     )
						A__				:       str	  =						TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""     )

						torch.manual_seed(0     )
						A__				:       Dict	  =						AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""     )

						torch.manual_seed(0     )
						A__				:       str	  =						UNetaDConditionModel(
						    sample_size=32       ,			layers_per_block=[1, 2]       ,			block_out_channels=[32, 64]       ,			down_block_types=[
						        """ResnetDownsampleBlock2D""",
						        """SimpleCrossAttnDownBlock2D""",
						    ]       ,			mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""       ,			up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]       ,			in_channels=6       ,			out_channels=6       ,			cross_attention_dim=32       ,			encoder_hid_dim=32       ,			attention_head_dim=8       ,			addition_embed_type="""text"""       ,			addition_embed_type_num_heads=2       ,			cross_attention_norm="""group_norm"""       ,			resnet_time_scale_shift="""scale_shift"""       ,			act_fn="""gelu"""       ,			class_embed_type="""timestep"""       ,			mid_block_scale_factor=1.4_1_4       ,			time_embedding_act_fn="""gelu"""       ,			time_embedding_dim=32       ,			)
						unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

						torch.manual_seed(0     )
						A__				:       Union[str, Any]	  =						DDPMScheduler(
						    num_train_timesteps=1000       ,			beta_schedule="""squaredcos_cap_v2"""       ,			beta_start=0.0_0_0_1       ,			beta_end=0.0_2       ,			thresholding=a_       ,			dynamic_thresholding_ratio=0.9_5       ,			sample_max_value=1.0       ,			prediction_type="""epsilon"""       ,			variance_type="""learned_range"""       ,			)

						torch.manual_seed(0     )
						A__				:       Optional[int]	  =						DDPMScheduler(
						    num_train_timesteps=1000       ,			beta_schedule="""squaredcos_cap_v2"""       ,			beta_start=0.0_0_0_1       ,			beta_end=0.0_2       ,			)

						torch.manual_seed(0     )
						A__				:       List[str]	  =						IFWatermarker()

						return {
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "unet": unet,
						    "scheduler": scheduler,
						    "image_noising_scheduler": image_noising_scheduler,
						    "watermarker": watermarker,
						    "safety_checker": None,
						    "feature_extractor": None,
						}






					def     __A     (					self     ):
						A__				:       Dict	  =						self.get_dummy_components()
						A__				:       Tuple	  =						self.pipeline_class(**a_     )
						pipe.to(a_     )
						pipe.set_progress_bar_config(disable=a_     )

						A__				:       Dict	  =						self.get_dummy_inputs(a_     )

						A__				:       Tuple	  =						inputs["prompt"]
						A__				:       Any	  =						inputs["generator"]
						A__				:       Dict	  =						inputs["num_inference_steps"]
						A__				:       List[Any]	  =						inputs["output_type"]

						if "image" in inputs:
							A__				:       Optional[int]	  =						inputs["image"]
						else:
							A__				:       List[str]	  =						None

						if "mask_image" in inputs:
							A__				:       int	  =						inputs["mask_image"]
						else:
							A__				:       Union[str, Any]	  =						None

						if "original_image" in inputs:
							A__				:       Optional[int]	  =						inputs["original_image"]
						else:
							A__				:       Optional[int]	  =						None

						A__				:       Union[str, Any]	  =						pipe.encode_prompt(a_     )

						# inputs with prompt converted to embeddings
						A__				:       int	  =						{
						    "prompt_embeds": prompt_embeds,
						    "negative_prompt_embeds": negative_prompt_embeds,
						    "generator": generator,
						    "num_inference_steps": num_inference_steps,
						    "output_type": output_type,
						}

						if image is not None:
							A__				:       List[Any]	  =						image

						if mask_image is not None:
							A__				:       Optional[int]	  =						mask_image

						if original_image is not None:
							A__				:       Tuple	  =						original_image

						# set all optional components to None
						for optional_component in pipe._optional_components:
							setattr(a_       ,			a_       ,			a_     )

						A__				:       str	  =						pipe(**a_     )[0]

						with tempfile.TemporaryDirectory() as tmpdir:
							pipe.save_pretrained(a_     )
							A__				:       str	  =						self.pipeline_class.from_pretrained(a_     )
							pipe_loaded.to(a_     )
							pipe_loaded.set_progress_bar_config(disable=a_     )

						pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

						for optional_component in pipe._optional_components:
							self.assertTrue(
							    getattr(a_       ,			a_     ) is None       ,			F"""`{optional_component}` did not stay set to None after loading."""       ,			)

						A__				:       List[str]	  =						self.get_dummy_inputs(a_     )

						A__				:       Optional[Any]	  =						inputs["generator"]
						A__				:       str	  =						inputs["num_inference_steps"]
						A__				:       Tuple	  =						inputs["output_type"]

						# inputs with prompt converted to embeddings
						A__				:       str	  =						{
						    "prompt_embeds": prompt_embeds,
						    "negative_prompt_embeds": negative_prompt_embeds,
						    "generator": generator,
						    "num_inference_steps": num_inference_steps,
						    "output_type": output_type,
						}

						if image is not None:
							A__				:       List[str]	  =						image

						if mask_image is not None:
							A__				:       List[Any]	  =						mask_image

						if original_image is not None:
							A__				:       str	  =						original_image

						A__				:       List[str]	  =						pipe_loaded(**a_     )[0]

						A__				:       Optional[int]	  =						np.abs(to_np(a_     ) - to_np(a_     )     ).max()
						self.assertLess(a_       ,			1e-4     )






					def     __A     (					self     ):
						A__				:       int	  =						self.get_dummy_components()
						A__				:       List[str]	  =						self.pipeline_class(**a_     )
						pipe.to(a_     )
						pipe.set_progress_bar_config(disable=a_     )

						A__				:       List[Any]	  =						self.get_dummy_inputs(a_     )
						A__				:       Optional[Any]	  =						pipe(**a_     )[0]

						with tempfile.TemporaryDirectory() as tmpdir:
							pipe.save_pretrained(a_     )
							A__				:       List[str]	  =						self.pipeline_class.from_pretrained(a_     )
							pipe_loaded.to(a_     )
							pipe_loaded.set_progress_bar_config(disable=a_     )

						pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

						A__				:       Union[str, Any]	  =						self.get_dummy_inputs(a_     )
						A__				:       Union[str, Any]	  =						pipe_loaded(**a_     )[0]

						A__				:       List[str]	  =						np.abs(to_np(a_     ) - to_np(a_     )     ).max()
						self.assertLess(a_       ,			1e-4     )










'''simple docstring'''






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "sew-d"



	def __init__( self		,   a_=32		,   a_=768		,   a_=12		,   a_=12		,   a_=3_072		,   a_=2		,   a_=512		,   a_=256		,   a_=True		,   a_=True		,   a_=("p2c", "c2p")		,   a_="layer_norm"		,   a_="gelu_python"		,   a_=0.1		,   a_=0.1		,   a_=0.1		,   a_=0.0		,   a_=0.1		,   a_=0.02		,   a_=1e-7		,   a_=1e-5		,   a_="group"		,   a_="gelu"		,   a_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,   a_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,   a_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,   a_=False		,   a_=128		,   a_=16		,   a_=True		,   a_=0.05		,   a_=10		,   a_=2		,   a_=0.0		,   a_=10		,   a_=0		,   a_="mean"		,   a_=False		,   a_=False		,   a_=256		,   a_=0		,   a_=1		,   a_=2		,   **a_		,   ):
			super().__init__(**a_		,   pad_token_id=a_		,   bos_token_id=a_		,   eos_token_id=a_						)
			lowerCAmelCase  :     Optional[int]       						= hidden_size
			lowerCAmelCase  :     Optional[int]       						= feat_extract_norm
			lowerCAmelCase  :     str       						= feat_extract_activation
			lowerCAmelCase  :     Dict       						= list(a_						)
			lowerCAmelCase  :     int       						= list(a_						)
			lowerCAmelCase  :     Any       						= list(a_						)
			lowerCAmelCase  :     Optional[Any]       						= conv_bias
			lowerCAmelCase  :     Tuple       						= num_conv_pos_embeddings
			lowerCAmelCase  :     int       						= num_conv_pos_embedding_groups
			lowerCAmelCase  :     Optional[int]       						= len(self.conv_dim						)
			lowerCAmelCase  :     Dict       						= num_hidden_layers
			lowerCAmelCase  :     str       						= intermediate_size
			lowerCAmelCase  :     Any       						= squeeze_factor
			lowerCAmelCase  :     Optional[int]       						= max_position_embeddings
			lowerCAmelCase  :     Tuple       						= position_buckets
			lowerCAmelCase  :     Union[str, Any]       						= share_att_key
			lowerCAmelCase  :     str       						= relative_attention
			lowerCAmelCase  :     List[str]       						= norm_rel_ebd
			lowerCAmelCase  :     int       						= list(a_						)
			lowerCAmelCase  :     str       						= hidden_act
			lowerCAmelCase  :     Dict       						= num_attention_heads
			lowerCAmelCase  :     Optional[Any]       						= hidden_dropout
			lowerCAmelCase  :     int       						= attention_dropout
			lowerCAmelCase  :     Optional[int]       						= activation_dropout
			lowerCAmelCase  :     Any       						= feat_proj_dropout
			lowerCAmelCase  :     Optional[int]       						= final_dropout
			lowerCAmelCase  :     Union[str, Any]       						= layer_norm_eps
			lowerCAmelCase  :     str       						= feature_layer_norm_eps
			lowerCAmelCase  :     Tuple       						= initializer_range
			lowerCAmelCase  :     int       						= vocab_size

			if (
			    (len(self.conv_stride						) != self.num_feat_extract_layers)
			    or (len(self.conv_kernel						) != self.num_feat_extract_layers)
			    or (len(self.conv_dim						) != self.num_feat_extract_layers)
			):
					raise ValueError(
					    "Configuration for convolutional layers is incorrect."
					    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
					    F'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
					    F'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

			# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
			lowerCAmelCase  :     List[str]       						= apply_spec_augment
			lowerCAmelCase  :     List[Any]       						= mask_time_prob
			lowerCAmelCase  :     Tuple       						= mask_time_length
			lowerCAmelCase  :     List[Any]       						= mask_time_min_masks
			lowerCAmelCase  :     List[str]       						= mask_feature_prob
			lowerCAmelCase  :     Union[str, Any]       						= mask_feature_length
			lowerCAmelCase  :     List[Any]       						= mask_feature_min_masks

			# ctc loss
			lowerCAmelCase  :     Any       						= ctc_loss_reduction
			lowerCAmelCase  :     str       						= ctc_zero_infinity

			# sequence classification
			lowerCAmelCase  :     Dict       						= use_weighted_layer_sum
			lowerCAmelCase  :     Tuple       						= classifier_proj_size





	@property
	def 	_lowerCamelCase     ( self						):
			return functools.reduce(operator.mul		,   self.conv_stride		,   1						)



